| 摘要: | 災難的救援搜索非常仰賴搜救團隊，他們冒著高風險進入災難環境中拯救受困人員。而科技的進步，例如光學、熱能、以及聲學感測等等也可以看到應用於搜救上的發明，使得協助搜救人員能夠更有效的、更安全的執行搜救任務。但在部份環境中，部分感測器難以運作，像是在火災中因為高溫以及濃煙無法運用熱能以及光學感測，或是在深山中樹高草雜，光學感測難以運行。而從上述提到的兩種環境下，體現出聲學感測的優勢。故本篇文章便是運用聲源定位系統的理念進行搜救裝置的開發，運用Matlab軟體模擬找尋出麥克風陣列幾何的最佳方案，使其具備體積小、全方位角度定位的優勢，以此設計出具有上述功能的立體菱形陣列，結合聲源定位演算法，其中含有轉向響應功率法(Steered Response Power Phase Transform, SRP-PHAT)以及新型頻率融合對角卸載法(Diagonal Unloading Beamformer with Novel Norm Transform, DU-NORT)，設計出能夠分別適應火災以及山難兩個災難環境下的雙系統。研究中在模擬環境下建構聲學模擬空間，測試面對不同的混響環境的抗噪能力，以及在現實環境下設計此兩種災難環境，分別加入該環境會有的噪音，像是火警鈴聲與強風聲，並透過立體菱形陣列與聲源定位演算法結合的系統進行實驗。由實驗結果獲得在火災環境中，使用SRP-PHAT演算法配合自適應聲音偵測器(Adaptive Sound Detection, ASD)能夠在最低訊噪比SNR = -10dB下，僅有10°的定位誤差，且在混響環境中，使用的定位演算法能在如教室般的室內空間，對該環境空間具有混響穩健性；而在山難環境中，使用DU-NORT演算法配合遮蔽抗噪系統(Mask)，能夠在訊噪比SNR = -25dB ~ -10dB下僅有22°的定位誤差。結果呈現系統能夠在火災的濃煙以及火警鈴干擾下，或是在山難的強風吹拂的影響下，有效的協助搜救團隊進行人員搜索的任務。;Search and rescue largely relies on the efforts of the search-and-rescue teams, who take high risks into disasters to rescue and search for victims. Advanced devices in technologies, such as optics, thermal, and acoustic sensors, have been applied in this field for search-and-rescue teams to operate more efficiently and safely. However, some devices are difficult to work in certain environments. For example, thermal and optical sensors cannot be fully functional in fire disaster due to high temperature and thick smoke. Optical sensors are hard to detect in mountain forests. From the two environments mentioned above, the advantages of the acoustic sensing stand out. Therefore, this thesis uses the concept of the sound source localization to develop search-and-rescue devices. In this research, we used Matlab simulation to find out the optimal solution of microphone array geometry for the advantages of small size and omnidirectional positioning. On this basis, we designed a three-dimensional diamond array, and combined it with two sound source localization algorithms, which are Diagonal Unloading Beamformer with Novel Norm Transform (DU-NORT), and Steered Response Power Phase Transform (SRP-PHAT), in two systems for fire and mountain disasters, respectively. In our experiments, we built an acoustic environment simulation to evaluate the robustness of reverberation. Also, we designed two realistic environments of fire and mountain disasters, and added noises of fire alarm and strong winds, respectively. Furthermore, we evaluated the performance of the systems combining the three-dimensional diamond array and sound source localization algorithms. The experimental results showed that using SRP-PHAT algorithm with Adaptive Sound Detector (ASD) in fire disaster can achieve only 10° localization error at the lowest signal-to-noise ratio (SNR) of -10dB, and can be applied in the indoor space of reverberation environment, such as classrooms. Using DU-NORT algorithm with noise robustness system called Mask in mountain disaster can achieve only 22° localization error at SNRs between -25 and -10dB. It demonstrated our systems can effectively assist search-and-rescue operations under the interference of thick smoke and fire alarm in fire disaster, or under the influence of strong winds in mountain disaster. |
